A Diffusion Tensor Imaging Tractography Algorithm Based on Navier-Stokes Fluid Mechanics

• Published in: IEEE transactions on medical imaging Vol. 28; no. 3; pp. 348 - 360
• Main Authors: Hageman, N.S., Toga, A.W., Narr, K.L., Shattuck, D.W.
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.03.2009; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Brain - anatomy & histology; Diffusion tensor imaging; Diffusion tensor imaging (DTI); Fluid flow; fluid mechanics; Geometry; Humans; Image Processing, Computer-Assisted - methods; Image segmentation; Imaging phantoms; Mechanics; Models, Neurological; Navier-Stokes equations; Partial differential equations; Phantoms, Imaging; Reproducibility of Results; Rheology - methods; Shape; Tensile stress; tractography; white matter
• ISSN: 0278-0062; 1558-254X; 1558-254X
• DOI: 10.1109/TMI.2008.2004403

We introduce a fluid mechanics based tractography method for estimating the most likely connection paths between points in diffusion tensor imaging (DTI) volumes. We customize the Navier-Stokes equations to include information from the diffusion tensor and simulate an artificial fluid flow through the DTI image volume. We then estimate the most likely connection paths between points in the DTI volume using a metric derived from the fluid velocity vector field. We validate our algorithm using digital DTI phantoms based on a helical shape. Our method segmented the structure of the phantom with less distortion than was produced using implementations of heat-based partial differential equation (PDE) and streamline based methods. In addition, our method was able to successfully segment divergent and crossing fiber geometries, closely following the ideal path through a digital helical phantom in the presence of multiple crossing tracts. To assess the performance of our algorithm on anatomical data, we applied our method to DTI volumes from normal human subjects. Our method produced paths that were consistent with both known anatomy and directionally encoded color images of the DTI dataset.